Factorization machines are a generic supervised method for a wide range of tasks in the field of artificial intelligence, such as prediction, inference, etc., which can effectively model feature interactions. However, handling combinations of features is expensive due to the exponential growth of feature interactions with the order. In nature, not all feature interactions are equally useful for prediction. Recently, a large number of methods that perform feature interaction selection have attracted great attention because of their effectiveness at filtering out useless feature interactions. Current feature interaction selection methods suffered from the following limitations: (1) they assume that all users share the same feature interactions; and (2) they select pairwise feature interactions only. In this paper, we propose novel Bayesian variable selection methods, targeting feature interaction selection for factorization machines, which effectively reduce the number of interactions. We study personalized feature interaction selection to account for individual preferences, and further extend the model to investigate higher-order feature interaction selection on higher-order factorization machines. We provide empirical evidence for the advantages of the proposed Bayesian feature interaction selection methods using different prediction tasks.

因式分解机是人工智能领域广泛任务的通用监督方法，例如预测、推理等，可以有效地对特征交互进行建模。然而，由于特征与订单交互的指数增长，处理特征组合的成本很高。本质上，并非所有特征交互对预测都同样有用。最近，大量执行特征交互选择的方法因其过滤无用特征交互的有效性而引起了极大的关注。当前的特征交互选择方法存在以下局限性：（1）假设所有用户共享相同的特征交互；(2) 他们只选择成对的特征交互。在本文中，我们提出了新颖的贝叶斯变量选择方法，针对分解机的特征交互选择，有效减少了交互次数。我们研究个性化特征交互选择以考虑个人偏好，并进一步扩展模型以研究高阶分解机上的高阶特征交互选择。我们提供了使用不同预测任务的贝叶斯特征交互选择方法的优势的经验证据。并进一步扩展模型以研究高阶分解机上的高阶特征交互选择。我们提供了使用不同预测任务的贝叶斯特征交互选择方法的优势的经验证据。并进一步扩展模型以研究高阶分解机上的高阶特征交互选择。我们提供了使用不同预测任务的贝叶斯特征交互选择方法的优势的经验证据。